In all other cases, the heuristic returns undecided.

the reported figure does not include initials and ordinal numbers because these subclasses of abbreviations cannot be discovered using these characteristics and have to be treated differently.


It simply assumes that all token-final periods in a test corpus represent sentence boundaries.

in order to exclude cases like ist and zu from being detected as collocates of a preceding sentence boundary, we add Table 1 Correct and incorrect frequent sentence starters.


In the first experiment, we tested how well Punkt performed when it was provided with the different abbreviation lists in addition to the abbreviations it was able to detect on the fly; see Table 14 for the results.

a human annotator will probably consider the period following the abbreviation as an abbreviation marker only and not as a sentence boundary period in analogy to other headlines.


According to this, if errors due to omitted translations and to omitted verbal forms are considered together, it is evident that errors involving omissions constitute the most important group, especially in the case of English-to-Spanish translations.

at every translation step, stacks are pruned to keep decoding tractable.


Chapters 7 and 8 were two of my favorites.

the authors never explicitly express the latter as one of their goals.


Other researchers, however, apply machine-learning techniques as off-the-shelf implementation, often with little knowledge about the algorithms and intricacies of data representation issues.

as noted earlier, TiMBL is a typical example of lazy learning since it does not abstract from the training data.


One of the most significant papers in this collection is by Phillip Cimiano, Aleksander Pivk, Lars Schmidt-Thieme, and Steffen Staab, entitled "Learning taxonomic relations from heterogeneous sources of evidence."

in their chapter "Unsupervised text mining for the learning of DOGMA-inspired ontologies," Marie-Laurie Reinberger and Peter Spyns cluster terms by using a shallow parser and identifying some types of relations by using prepositions.


It is not obvious that this will work for purely linguistic regularities, and when the task is to discover properties of a non-linguistic reality, it is clear that not everybody is willing to bet on it.


There are two aspects of annotation tasks that make them more difficult than they might seem at first blush.

for the foreseeable future we are stuck with annotation.


Especially in terms of IROC, this method is clearly superior to the other confidence measures.

unlike the CER, the ROC curve is independent of the prior probability of the two classes correct and incorrect.


In such a word graph, the first word of a target phrase is assigned the score for the whole phrase.

δ(e, e i+t i ) denotes an extension of the Kronecker delta: The value calculated in Equation 16is not normalized.


The statistical models presented in Section 2.2 can be used to estimate the confidence of target words as first described in Ueffing and Ney (2005b).

it can be seen that WER is the stricter error measure: it considers fewer words as correct than PER does.


The article is organized as follows: In Section 2, we briefly review the statistical approach to machine translation.

we determine the maximal translation probability of the target word e over the source sentence words: where f 0 is the "empty" source word (Brown et al.


The nature of a particular restricted domain affects the kinds of questions asked and answers that can be expected.

the focus of this article is on the use of restricted domains for automated question answering.


A clear example is the UC project developed in the 1980s.

the set of senses available to words is typically a subset of all the available senses.


The best management advice is to raise awareness and strive for prevention.

this situation poses a serious bottleneck to "advanced" question-answering systems.


This answer was accepted by both physicians because it clearly states that specific analgesics are most likely to cause the problem, and gives a direct guideline for preventive treatment.

naturally, the physicians would need to follow up and examine the source citation in more detail. )


The treatment profile (Taxol) and the outcome measure (survival rate) have a content that can be easily specified-a single choice from a menu would suffice.

the user interface is a simple HtML-based application that allows users to select the type of data they want to query and to specify constraints on it.


The user need not be aware of the underlying formalism any more than a person using a text editor need be aware of ASCII codes.

application of the same test showed no significant difference in subjects' performance on the second versus third, second versus fourth, or the third versus fourth composed query.


In any case, the book will almost certainly fertilize ongoing research in the rapidly expanding area of biomedical NLP, so I felt that studying it was time well spent.

i often found it hard to keep track of literature reviewed in different chapters.


I enjoyed reading this book, and not just because it agreed with many of my own prejudices.

in an example of "landing site" coercion like Amelie left for an hour the entailment that Amelie left is preserved.


10 Glimpse supports approximate and exact matching, Boolean queries, wild cards, regular expressions, and many other options.

the results in table 7 furthermore reveal that WSD performance exceeds sense ranking accuracy in high-frequency bands (most notably in Band 1,000-5,000), which seems counterintuitive.


In these semantic space models, contexts are defined over words bearing a syntactic relationship to the target words of interest.

given the importance of WSD for basic NLP tasks and multilingual applications, a variety of approaches have been proposed for disambiguating word senses.


r gram-rel (v gram-rel , see Equation (12)) uses the obliqueness hierarchy (Keenan and Comrie 1977) to rank paths according to the salience of their grammatical relations.

avgambig Types acc sr acc wsd our semantic space is built on smaller corpora.


This method has the potential to be very fast: linear in k. However, because the number of possible translations is exponential in n, we may have to set k extremely high in order to find the true best translation (taking the LM into account) or something acceptably close to it.

then, the third item is enumerated and merged with the first (unlike in the k-best algorithm).


These functions let us correctly calculate the LM score of a sentence piecemeal.

in the +LM parser, the score of an item [X, i, j; e] in the chart does not reflect the LM probability of generating the first (m − 1) words of e. two items [X, i, j; e] and [X, i, j; e ] are not directly comparable.


Values of p and q in the "cgisf" example.

it is considerably more powerful than a conventional phrase pair.


A search path is a series of steps in the search for a referent, representing visits to nodes in the domain tree D. The path will be modeled by an ordered list of visited nodes: O = n 1 , n 2 , .

we want to consider situations in which FI and SL have produced a complete description, and investigate how much gain has been made by using this compared to a less complete description that is still more complete than MD.


Again, we shall focus on cases where n = 1, and omit brackets.

another option would be to treat a relation like "being in room 120" as a one-place property of the library, and to use the Incremental algorithm (Dale and Reiter 1995) to generate the descriptions in question.


In the second step, an approximationĝ(a) of this unknown evaluation function g(a) is learned from training data, using some machine-learning scheme.

one could argue that even at a word level, deterministic, non-packing parsing is not always possible, but Nivre sticks to his guns.


In short, 2005 CL authors were much less influenced by other research communities investigating language than 1995 CL authors.

i extracted all citations from Computational Linguistics (CL) articles (excluding book reviews) in these years to journal papers.


If we wish to increase the quality of a word alignment, we allow the alignment process access to extra data, which is used only during the alignment process and then removed.

these models generally have higher precision and lower recall than IBM Model 4 symmetrized using the "Refined" or "Union" heuristics.


1993) is a vital component of all statistical machine translation (SMT) approaches.

aER does not share the property of F-Measure (with α = 0.5) that unbalanced precision and recall are penalized.


Web search engines produce estimates of page hits, as illustrated in Tables 1-3.

joint frequencies ought to decrease monotonically as we add terms to the query.


It will be shown, both theoretically and empirically, that our proposed method reduces the mean square error (MSE) by about 50%.

given a data matrix A of n rows and D columns, brute force computation of AA T would cost O(n 2 D), or more efficiently, O(n 2f ), wheref is the average number of non-zeros among all rows of A. Brute force could be very timeconsuming.


we can construct the random sample by picking any D s documents.


*T* traces are also used for wh-questions: (SQ (VBP do) (NP-SBJ Americans) (VP (VB favor) (NP (-NONE-*T*-1)) (ADVP most) (NP-TMP these days))) (.

all dependencies are defined in terms of the heads of lexical functor categories and of their arguments.


Statistical parsing on the Penn Treebank has made great progress by focusing on the machine-learning or algorithmic aspects (Magerman 1994;Ratnaparkhi 1998;Collins 1999;Charniak 2000;Henderson 2004;McDonald, Crammer, and Pereira 2005).

the null element *PPA* ("Permanent Predictable Ambiguity," 26 occurrences) is used for genuine attachment ambiguities.


Without function composition, the category of regularly would have to be ((S\NP)/NP)\((S\NP)/NP), but (crossed) composition allows the ordinary category (S\NP)\(S\NP) to also work in this case.

this is crucial to correctly inducing and evaluating grammars and parsers for any expressive formalism, including tAG, GPSG, HPSG, LFG, and MPG.


It is available from the Linguistic Data Consortium, and has been used to train widecoverage statistical parsers that obtain state-of-the-art rates of dependency recovery.

such examples are relatively frequent: There are 97 instances of ((S[.


Language change, as with most changes in a large population, has often been believed to follow a logistic S-shaped curve.

these are built upon the theories of Cavalli-Sforza and Feldman (1981), in which a set of cultural traits with a limited number of parameter settings is passed on between individuals.


In (5b), the phrase an innocent blonde may be understood in two ways.


It is a requirement that she be 17 years old.

in his course, we learned formal language theory from notes that eventually became a book (Wall 1972), a bit of Fortran and COMiT, a language developed by Victor Yngve at MiT.


Chi (Proposition 4) and Abney,McAllester,and Pereira (Lemma 5) showed that P Z<∞ = P PCFG , namely, that every WCFG probability distribution is in fact generated by some PCFG.

in recent years the field of computational linguistics has turned to machine learning to aid in the development of accurate tools for language processing.


These sentences are not used in the training process.

the philosophy in earlier work which combined the supertagger and parser (Clark, Hockenmaier, and Steedman 2002;Clark and Curran 2003) was to use an unrestrictive setting of the supertagger, but still allow a reasonable compromise between speed and accuracy.


The evaluation on DepBank raises a number of issues regarding parser evaluation.

this is not a problem for Johnson et al.


The idea was to give the parser the greatest possibility of finding the correct parse, by initializing it with as many lexical categories as possible, but still retain reasonable efficiency.

one question we are often asked is whether the CCG derivations output by the parser could be converted to Penn Treebank-style trees to enable a comparison with, for example, the Collins and Charniak parsers.


So we did inspect the annotation in DepBank, and compared it to the transformed CCG dependencies, but only the gold-standard CCG dependencies.

as far as we know, this is the first application of Goodman's approach to finding highest scoring dependency structures.


the meaning of parse differs in the two cases.


This result demonstrates the significant amount of syntactic information encoded in the lexical categories.

the scores at the most general level in the GR hierarchy (dependent) correspond to unlabeled accuracy scores.


The predicate-argument dependencies are represented as 5-tuples: , where h f is the lexical item of the lexical category expressing the dependency relation, f is the lexical category, s is the argument slot, h a is the head word of the argument, and l encodes whether the dependency is non-local.

the results in Table 12 add considerable weight to one of our main claims in this article, namely, that highly efficient parsing is possible with CCG, and that largescale processing is possible with linguistically motivated grammars.


For example, the first sense of star as derived from SemCor is celestial body, but if one were disambiguating popular news stories then celebrity would be more likely.

r Lesk's semantic similarity score (Banerjee and Pedersen 2002, lesk) produces particularly good results for nouns which have low corpus frequencies; Jiang and Conrath's (1997, jcn) score does well on higher frequency words.


Figures 2 and 3 show the precision for type and token (WSD) evaluation where the items have a frequency at or below given thresholds in SemCor.

competing Two or more related senses are ranked highly but they are overtaken by an unrelated sense.


Data for the heuristic can come from either dictionaries or a sample of sensetagged data.

this total is divided between the senses of the target word by apportioning the distributional similarity of each neighbor to the senses.


Evaluation is performed on an artificially constructed test set from unambiguous words in the same category as the 27 test words (nouns, verbs, and adjectives).

for nouns with a frequency of five or less (12.9% of the polysemous nouns in the Senseval-2 data) it outperforms the SemCor first sense heuristic.


Every synset in WordNet's sense inventory is annotated with at least one domain label, selected from a set of about 200 labels.

the performance of these systems is strongly dependent on the quantity of training data available (Yarowsky and Florian 2002), and manually sense-annotated text is extremely costly to produce (Kilgarriff 1998).


1 It might be argued that although Wolf and Gibson allow re-entrancy and crossing dependencies, they do not oblige an analyst to find such phenomena in a text, and therefore that the presence of these phenomena still constitutes an empirical result.

are these analyses convincing evidence against a tree-based model of text structure?


The notion of transfer is supposed to explain children's nonlinear learning curve, as well as their ability to generalize their lexical knowledge to novel items.

its proposed view of syntax could be validated through computational modeling.


Reviewing under such conditions isn't intrinsically wasteful, but the quality of the reviews must suffer.

at such a conference, we would still get to schmooze until all hours with colleagues we haven't seen in ages; and company reps would still get to promote their new products and convince the best and brightest of our students to come work for them.


Although I believe that these criticisms of journals are correct, it doesn't seem as though there are too few journals around to publish all the papers that people want to submit.

is there no credit to be gained from really being the first to publish some new method or theory or some clever take on an old one?


Next, we give details regarding the corpus used for our experiments, describe the methods used for comparison with our approach, and note the evaluation metric employed for assessing model performance.

it is therefore not surprising that coreference resolution delivers performance gains on the first two tasks but not on the latter (see Table 5 in Section 4 and Table 10 in Section 6.3).


First, note that the entity-grid model significantly outperforms LSA on both domains (p < .01 using a Sign test, see Table 5).

in our experiments, we built two content models, one for the Accidents corpus and one for the Earthquake corpus.


The resulting training set consists of 33,604 sentences (when n = 10 and = 0.95; see Section 5.4 for details).

the preliminary model was used to reduce the search space for parsing a training treebank.


These errors were mainly caused by the indirect effects of other errors.

this might increase the size of parse forests and therefore decrease parsing efficiency, because differences between additional attributes interfere with equivalence relations for ambiguity packing.


The book is well written and addresses both the theory and practice of text mining, which makes it appealing for researchers and practitioners alike.

text mining is a highly interdisciplinary research area, bringing together research insights from the fields of data mining, natural language processing, machine learning, and information retrieval.


Rada Mihalcea is an Assistant Professor of Computer Science at the University of North Texas.

text mining is closely related to the older area of data mining, which targets the extraction of interesting information from data records, although text mining is allegedly more difficult, as the source data consists of unstructured collections of documents rather than structured databases.


1 Work on embodiment and conceptualization, new insights into societal grounding of conceptual representations (e.g., DeVault, Oved, and Stone 2006), empirical and computational studies on generation (both incremental and non-incremental, from numerical data; e.g., van Deemter 2006), and recent experimental techniques for studying language production (see Roelofs 2004 for an overview) give a sense that this book could be part of an exciting revival of cognitively motivated NLG.

part B ("Incrementality") traces the roots of the notion of incrementality in computer science, and provides an extensive overview of various notions of incrementality.


Moreover, some simple rules can be used to join constituents and fix a considerable portion of the mismatches (Toutanova, Haghighi, and Manning 2005).

finally, although predicates are typically expressed as verbs, and thus much work in both linguistics and SRL focuses on them, some nouns and adjectives may be used predicatively, assigning their own roles to entities (as in the adjective phrase proud that we finished the paper, where the subordinate clause is a Theme argument of the adjective proud).


From the confusion matrix in Figure 7(b), we can see that the largest number of errors are confusions of argument labels with NONE.


It turns out that enforcing the non-overlapping constraint does not lead to large gains in performance.

for COARSEARGM ALL we count {0} as a true positive span, {1, 2} , {3, 4}, and {7, 8, 9} as false positive, and {1, 2, 3, 4} and {7, 8, 9} as false negative.


Rows Poly+AST 1 and Poly+AST m 1 investigate this possibility by presenting the combination of polynomial and tree kernels.

the results for boundary recognition, classification, and re-ranking stages provide systematic evidence about the significant impact of tree kernels on the overall accuracy, especially when the amount of training data is small.


For the complete SRL task, we used the previous BC and all the available data, that is, the sections from 2 to 21, for training the role multiclassifier.

still, although we only used a small fraction of the available training data (i. e., only 2 sections out of 22 were used to train the re-ranker) our system's accuracy is in line with state-of-the-art systems (Carreras and Màrquez 2005) that do not employ tree kernels.


For example, [VP [V NP]] is an SST which has two non-terminal symbols, V and NP, as leaves.

in case more than one structure is selected as correct, we choose the one associated with the highest SVM score.


More importantly, the arguments of nominalized predicates have a much more uneven distribution: Arguments of a nominalized predicate can occur either inside the NP headed by the predicate or outside when a support verb is present (see Section 2 for examples).

toutanova, Haghighi, and Manning (2005) address this by using a joint-learning strategy to rule out such conflicting argument labels.


On the same machine, the average test time for each stage, excluding feature extraction, is around 2 minutes.

most of the features used in our system are common features for the SRL task.


This strategy is upper-bounded by the number of correct arguments that agree with some constituent.

table 10 shows the results of using only constraints 1, 2, 3, and 4.


It is unclear how this feature can be mimicked because it relies on the internal structure of a full parse tree.

as we have shown, it can be used to resolve conflicting argument predictions in an individual system but can also serve as an effective and simple approach to combining different SRL systems, resulting in a significant improvement in performance.


Full parsing information helps in argument identification.

(2005) (their earlier version appeared in Pradhan et al.


The underlying system architecture can largely affect the outcome of our study.

r Shallow-Subcategorization: describes the chunk and clause structure around the predicate's parent in the pseudo-parse tree.


The two-step constituent-by-constituent architecture became a common blueprint for many systems that followed.

for features that need full parse trees in their extraction procedures, we either try to mimic them with some heuristic rules or discard them.


r Context words and POS tags of the context words: the feature includes the two words before and after the constituent, and their POS tags.

the probability of the "null" class is set to be 0.55 based on empirical tests, and the probabilities of the remaining classes are set based on their occurrence frequencies in the training data.


Although the completion of the manuscript admittedly antedates much of the ongoing work in the field (e.g., the application of deep linguistic processing in the analysis of biomedical and other scientific texts), a comparison of DBS to wide-coverage systems such as the LinGO English Resource Grammar (Copestake and Flickinger 2000) (including also related activities such as the development of Robust Minimal Recursion Semantics [Copestake 2007]) or Alpino (analysis of unrestricted Dutch texts [Bouma, van Noord, and Malouf 2001]) would have been a welcome complementation to the presentation of DBS in the book.

as such an underlying format, it can be used to describe linguistic as well as extralinguistic content (to represent utterances and the context, respectively).


The second fragment extends the coverage to pronouns, complex NPs (Det-Adj*-N), and transitive and ditransitive verbs in simple and complex tenses.

the book does not offer much discussion of the relation between the proposed analysis and competing approaches.


For data with a very strong correlation between the input features A and the output labels B, the turning point below which performance is spuriously high occurs at around κ = 0.55 (Figure 3d), a value the community holds to be pretty low but which is not unknown in published work.

the data actually available for analysis is shown in the middle of the figure.


For the non-anaphors [ 3 Schwartz] and [ 6 the debt market], we generate two sets of "00" instances.

traditionally, learning-based approaches to anaphora resolution adopt the single-candidate model, in which the potential antecedents (i.e., antecedent candidates) are considered in isolation for both learning and resolution.


From the two trees, we can see that both models rely on similar features such as lexical, positional, and grammatical properties for pronoun resolution.

the referring expression used is called the anaphor and the expression being referred to is its antecedent.


Both of these studies were carried out on uncommon data sets, which makes it difficult to compare their results with other baseline systems.

compared with the pronoun resolution described in the previous section, here we find that for non-pronoun resolution the superiority of the twin-candidate model against the single-candidate model is not apparent.


Unlabeled and labeled attachment scores for limited lexicalization (n = count, d = number of distinct roots).

we can say that the parser has no problem in finding adjacent head-initial dependencies.


The start state then becomes q.TOP, and the q rules are rewritten to specify the current node.

the ranked label of a node is the pair labelandrank t (p) ≡ (label t (p), rank t (p)).


In the traditional statement of T, sources(r) are the variables x i , standing for the i th child of the root at path (i), and the right hand sides of rules refer to them by name: (q i , x i ).

we can simulate lookahead using states, as in these productions:  By omitting rules like qpro NP → ..., we ensure that the entire production sequence will dead-end unless the first child of the input tree is in fact PRO.


A sample of these parameter value differences can be seen in Figure 10.

the JJ(JJ NN) and NN(JJ NN) reordering rules described previously are modified as follows: All reordering rules with the same input and output variable sequence are in the same tied class, and thus receive the same probability, independent of their parent symbols.


In all the experiments described in this article we use SNoW (Roth 1998;Carlson et al.

another reason might be the fact that in arabic word formation is a more complicated process, for example by allowing more characters to occur in the word between the root letters as previously mentioned.


r There are 28 letters in Arabic which are represented using approximately 40 characters in the transliteration of Modern Standard Arabic orthography of Buckwalter (2002).

we are unaware of any set of rules which attempts to address this task, or of the chances of solving this problem deterministically.


Similarly, the middle value is set to 0.2416 and the low to 0.0155.

experiment B inherently assumes that any sequence of three consonants can be the root of a given word.


We therefore trained the classifiers to consider as targets, during training and testing, only letters that occurred in the observed word, plus w, i, n, and l (depending on the radical), rather than any of the alphabet letters.

the irregular roots n.p.l, i.c.g, q.w.m, and g.n.n in this pattern yield the seemingly similar forms hplh, hcgh, hqmh, and hgnh, respectively.


Despite its increasing importance, semi-supervised learning is not a topic that is typically discussed in introductory machine learning texts (e.g., Mitchell 1997; Alpaydin 2004) or NLP texts (e.g., Manning and Schütze 1999; Jurafsky and Martin 2000).

in addition, i like the organization of the book.


The remaining chapters focus roughly on six types of semi-supervised learning methods: 2 r Self-training.

1 to learn about semi-supervised learning research, one has to consult the machine-learning literature.


However, the other path is to accept (and in fact insist) that highly detailed empirical studies must be reproducible to be credible, and that it is unreasonable to expect that reproducibility be possible based on the description provided in a publication.

at that point the task of converting our code into a well-documented and easy to use package is often nearly impossible.


Mark Lee and a number of my students have created implementations of this approach and linked it to dialogue and other applications, but there has been no major application showing its essential role in a functioning conversational theory where complex belief states are created in real time.

like others, I came to see this assumption was quite untrue, and myself moved towards Machine learning (Ml) approaches to word-sense disambiguation (e.g., Stevenson and Wilks 2001) and I now work in Ml methods applied to dialogue corpora (as I shall mention subsequently).


It took me twenty years to get the required permissions but the volume finally appeared in 2005 (Masterman et al.

there are those in AI who say they have ignored much of the last 40 years of AI research that would have helped them.


The method proposed in this article for learning a policy from a pre-existing corpus of dialogues can be used to find such an initial policy.

this "pure RL" policy simply selects the action a with the highest Q data (s, a) given the state s. As demonstrated by the evaluation in Section 3, this policy performs very badly.


Indeed, this is often considered an integral part of RL.

this would certainly be a problem if this metric were to be used to compare different systems within the COMMUNICAtOR data set.


Although other scoring metrics are clearly possible, this one is a simple and reasonable approximation of the relative expected correctness of confirmed versus non-confirmed information in dialogue systems.

there is no apparent reason to believe that this fact biases our results towards one type of system or another.


While RIGHT-ARC n l allows the dependent j to seek dependents to the left of i, by simply moving i from λ 1 to λ 2 , RIGHT-ARC p l essentially incorporates a SHIFT λ transition by moving j to λ 1 |i, because any dependency arc linking j to a node preceding i would violate PROJECTIVITY.

both the stack and the buffer will be represented as lists, although the stack will have its head (or top) to the right for reasons of perspicuity.


The arc-eager parser differs from the arc-standard one by attaching right dependents (using RIGHT-ARC e l transitions) as soon as possible, that is, before the right dependent has found all its right dependents.

when discussing PROJECTIVITY, we will often use the notation i → * j to mean that j is reachable from i in the reflexive and transitive closure of the arc relation.


The list λ 1 has its head to the right and stores nodes in descending order, and the list λ 2 has its head to the left and stores nodes in ascending order.

we have defined four different transition systems for incremental dependency parsing, proven their correctness for different classes of dependency graphs, and analyzed their time and space complexity under the assumption that there exists a constant-time oracle for predicting the next transition.


7.3 : so we ship one 7.4 : boxcar 7.5 : of oranges to Elmira 7.6 : and that takes another 2 hours Previous studies of discourse deixis annotation showed that these are extremely difficult judgments to make (Eckert and Strube 2000;Navarretta 2000;Byron 2002), except perhaps for identifying the type of object (Poesio and Modjeska 2005), so we simplified the task by only requiring our participants to identify the boundaries of the area of text in which the antecedent was introduced.

for example, it assumes that units may not overlap in a single coder's output, yet in practice there are many The difference between overlapping units is d(A, B) = s 2 − + s 2 + (adapted from Krippendorff 1995, figure 4, page 61).


The phrase extraction heuristic allows each contiguous span in a sentence to be aligned to either zero or one span in the other sentence; that is, nominating a phrase pair precludes the choice of many other possible phrase pairs.

first, it is infeasible to perform frequent evaluations when assessing incremental system changes or tuning system parameters.


The italics denote lexically identical phrase pairs.

relatively few of the subsets of the full set of possible phrase pairs are valid.


This is not to say that going electronic-only makes the journal free of production costs.

who needs a static survey article in a journal in an era of web resources that are constantly kept up-to-date?


As in the MT experiments, the results are comparable to those obtained on the Xerox corpus, with the exception of the English-Spanish pair.

the IPMt paradigm proposed in this article allows for a close collaboration between a human translator and a machine translation system.


Karamanis and Mellish (2005b) conducted an experiment in the MPIRO domain using Lapata's methodology which supplements the work reported in this article.

work on text generation which is solely based on rhetorical relations (Hovy 1988;Marcu 1997, among others) typically masks entity coherence under the ELABORATION relation.


The number that a constraint C i assigns to a candidate indicates how many times that candidate violates C i .

candidate a is optimal according to ranking R iff no other candidate generated by GEN is more harmonic than a.


Proof : Suppose every partial negation of Ω is consistent.

the question posed at the outset of this article was: for a constraint set CON with k constraints that map candidates to natural numbers, what is the cardinality of the largest set of candidates S such that, for each subset t ⊆ S, there is at least one ranking R CON under which every t in t is optimal, but no s in S − t is optimal?


Other forms of the expression, such as pull the weights, typically are only used with a literal meaning.

development expressions are used in devising the fixedness measures, as well as in determining the values of their parameters as explained in the next subsection.


12 PMI is known to perform poorly on low frequency items.

the main point of the assumption here is that VNICs are more likely to appear in fixed forms (known as their canonical forms), more so than non-idiomatic phrases.


There is much evidence in the linguistics literature that idiomatic combinations also have idiosyncratic lexical and syntactic behavior.

most idiomatic usages of expressions in DT I high appear in a canonical form, and most usages in a canonical form are idiomatic.


Section 7.1 explains the details of our experimental setup.

to provide gold-standard data for each instance of an expression, we first divide the other instances (of the same expression) into likelyidiomatic and likely-literal groups, where the former group contains usages in canonical form(s) and the latter contains usages in non-canonical form(s).


In this approach, the functionalqualitative analysis that provides an interpretation of the discourse types is performed as a later step.

the bottom-up method can be easily applied to large corpora and is replicable, whereas the top-down approach is more subjective.


Prepositions have tended to be overlooked in NLP applications, but there have been isolated examples of prepositions being shown to be worthy of dedicated treatment.

similarly, the emergence of resources such as the Preposition Project, and the data set made available for the semEval 2007 task on the word sense disambiguation of prepositions (Litkowski and Hargraves 2007), provide the means for more detailed analysis of preposition semantics.


This is the topic of the third article in this special issue: Jørgensen and Lønning propose a unification-based grammar implementation of a formal semantic analysis of preposition semantics, and demonstrate its application in Norwegian→English MT.

combinations of pure locative relation markers and prepositions which are specified for place values are ruled out, and no embedded locative relations are allowed in the semantic structure of a single locative expression.


In all, 37 conceptual relations were defined.

first, we discuss similar work involving hypernyms.


The information in Factotum complements WordNet through the inclusion of more functional relations (e.g., non-hierarchical relations such as uses and is-function-of ).

(2) The industry already operates on very thin margins.


Mohit and Narayanan (2003) use WordNet hypernyms to generalize patterns for information extraction inferred from FrameNet annotations by distributing support from terms co-occurring in annotations for frame elements to the terms for hypernyms.

future work will also investigate improvements to the application to definition analysis.


It turns out that grounds and news are often used as the prepositional object in PTB when the sense for on is purpose (or reason).

this occurs because several of the miscellaneous roles in FrameNet cover subtle distinctions that are not relevant for definition analysis (e.g., cognizer and addressee).


Unlike the general case for WSD, the sense inventory is the same for all the words being disambiguated; therefore, a single classifier can be produced rather than individual classifiers.

in the experiments discussed below, a potential collocate (coll) is selected whenever the conditional probability for the class (C) value exceeds the prior probability by a factor greater than 20%: 6 for a given potential collocation word (coll) to be treated as one of the actual collocation words, the relative percent change of the class conditional probability (P(C|coll)) versus the prior probability for the class value (P(C)) must be 20% or higher.


Because the source term and target terms might occur in other relationships, associations based on them alone might not be as accurate.

the other inventories are discussed because each provides relation types incorporated into the inventory used below for the definition analysis (see Section 3.5).


Likewise, characteristic is the preferred role for of, but category is interpretable.

the FrameNet distinctions are quite specific: there are 192 distinctions associated with for (21 with low-frequency pruning); and, there are 17 distinctions in Factotum (15 with low-frequency pruning).


In cases where noun senses were not enough for relation selection, the annotators had to rely on a larger context provided by the sentence and its translations.

in Romanian the genitive case is realized either as a suffix attached to the modifier noun N 2 or as one of the genitival articles a/al/ale.


For example, both instances family#2 estate#2 (POSSESSION) and the sister#1 of the boy#1 (KINSHIP) are mapped into entity#1 -entity#1.

the translation of nominal phrase and compound instances from one language to another is highly correlated with the structure of each language, or set of languages.


The processing method consists of a set of iterative procedures of specialization of the examples on the WordNet IS-A hierarchy.

many of them encoded TYPE relations (e.g., member state, framework law) which, most of the time, are encoded by N N patterns in the target languages (stato membro and legge quadro in Italian, respectively).


The net effect is that the semantic relations that were attached to f ij will be 'scattered' across the new specialized features which form the second boundary.

for a better understanding of the annotation results we also computed the percentage agreement, which is indicated for each classification set in parentheses in Table 3.


"-" means the semantic relation is not encoded by the syntactic construction, " " and "x" symbols indicate whether the relation is or is not symmetric.

unlike Herskovits, we do not consider pragmatic issues such as relevance and tolerance.


Another example is the love of children and not the love by the children, where children are the recipients of love, not its experiencers.

we presented empirical observations on the distribution of nominal phrases and compounds and the distribution of their meanings on two different corpora, based on two state-of-the-art classification tag sets: Lauer's set of eight prepositions (Lauer 1995) and our list of 22 semantic relations.


In Finnish one uses expressions which literally when translated into English would become forgot my wallet into my car, and in English we have formulations like a bridge from Buda to Pest.

our implementation is of a fragment of Norwegian.


Some languages use pre-or postpositions, whereas other languages use locative case.

we are also grateful to the editors of the special issue and to the reviewers for their helpful advice.


The main goals for MRS is to be expressively adequate allowing linguistic meaning to be represented properly, to be compatible with other modules in a grammar, to be computational tractable, and to support underspecification.

we arrive at the same semantics for both sentences in Example (8).


This in turn calls the basic incremental algorithm, Algorithm 1, which will return the property ball.

at different stages during a dialog the dialog manager may recognize that the system needs to generate a response to the last input utterance.


We will begin by reviewing the experimental data describing topological spatial prepositions.

one basic constraint on landmark selection is that the landmark should be distinguishable from the target.


The parse tree on the left was generated using a probabilistic wide-coverage LFG parser.

before these higher-level frameworks can be applied to real-world data, a model of proximity that is capable of segmenting a region at the metric or geometric level is required.


To generate such locative expressions we repeatedly call the basic incremental algorithm for a sequence of different possible spatial relations.

the cost of being able to generate these embedded descriptions is a higher exponential complexity.


These three chapters will most likely be of least interest to the computational linguistics community.

among the topics discussed are spam, types of user information needs, Web crawling and indexing, linkbased approaches to document ranking such as PageRank, Markov chains, and hubs and authorities.


Chapter 8 is devoted to evaluation in IR, and presents major evaluation frameworks, such as the Text Retrieval Conference (TREC) and classical evaluation measures, such as mean average precision, precision at different cutoff points, as well as the more recently developed measure NDCG (normalized discounted cumulative gain) for evaluation with graded (non-binary) relevance judgments.

two topics that might be of interest are the use of wildcard queries by users and spelling correction of queries, discussed in Chapter 3.


When domain-specific training corpora are not available, a referential semantic interface could still exploit its model of the world: the data to which it is an interface, and patterns characterizing these data.

each query returns a destination referent e S such that S is a subset of the context set in the original world model M * .


In first-order domains, for example, this may require evaluating certain binary relations over all pairs of individuals in the world model, with time complexity proportional to the square of the size of the world model domain.

the result of conjoining a property l with a context set e can therefore be found by downward traversal of an edge in this lattice labeled l and departing from e. 6 in Figure 4, the set of executables that are read-only would be reachable by traversing a READ-ONLY relation from the set of executables, or by traversing an EX-ECUtABLE relation from the set of read-only objects, or by a composed path READ-ONLY•EXECUtABLE or EXECUtABLE•READ-ONLY from e .


These include techniques for building effective language models for speech recognition when little or no multimodal training data is available, and techniques for robust multimodal understanding that draw on classification, machine translation, and sequence edit methods.

it attempts to classify the utterance and identifies substrings of the input as slotfiller values to construct a frame-like semantic representation.


The first path through the lattice results from the combination of the speech string show chinese restaurants here with an area gesture.

comparing the strengths and weaknesses of these techniques is very difficult.


The assignment of meaning to a multimodal output is achieved by parsing the utterance using the grammar.

data-driven approaches are robust and provide a simple process of developing applications given availability of data from the application domain.


It follows that a larger lexicon will have a greater coverage of sentiment expressions.

a neutral expression intensity indicates that the direct subjective phrase itself is not contributing to the expression of the private state.


The conj polarity feature determines if the clue instance is in a conjunction.

the modifies strongsubj feature is true if the clue instance and its parent share an adj, mod, or vmod relationship, and if its parent is an instance of a strongsubj clue from the lexicon.


Second, which of the possible sentiment expressions actually are expressing sentiments?

what the polarity of a given word or phrase is when it is used in a particular context is another problem entirely.


Recall from Section 2.2 that an active feature is a feature that is strongly associated with the word, that is, its (initial) weight is higher than an empirically predefined threshold, θ weight .

in the current work we use Mi for data analysis, and for the evaluations of vector quality and word similarity performance.


A demonstration of such potential appears in Geffet and Dagan (2005), which presents a novel feature inclusion scheme for vector comparison.

similarity measures which have been used in the cited papers include weighted Jaccard (Grefenstette 1994), Cosine (Ruge 1992), and various information theoretic measures, as introduced and reviewed in Lee (1997Lee ( , 1999.


Because the lexical entailment relation is directional, each candidate pair was duplicated to create two directional pairs, yielding a test set of 3,200 pairs.

table 9 Bootstrapped weighting: top 10 common features for country-state and country-party along with their corresponding ranks in the two (sorted) feature vectors.


Following these observations, we developed a bootstrapping formula that improves the original feature weights (Section 4), leading to better feature vectors and better similarity predictions.

there are only two common features (among the top 100 features) for the incorrect pair country-party, both with quite low ranks (compare with Table 7), while the rest of the common features for this pair did not pass the top 100 cutoff.


Utilizing a refined definition of substitutable lexical entailment both as an end goal and as an analysis vehicle for distributional similarity.

these findings suggest a direct indication of an improved quality of the bootstrapped feature vectors.


To this end, the noun n is substituted in turn with each of its k distributionally most similar nouns, n i , and then both of the obtained "similar" pairs n i , v and n i , v are sought in the training set.

after empirical tuning of weights for the arithmetic mean, Weeds and Weir's formula practically reduces to Lin's measure, as was anticipated by their own analysis (in Section 4 of their paper).


To help someone fix their program requires that you briefly enter their world, and align your conceptual model of the problem with theirs, and point the way forward.

the discovery of a new word in a text may require an update to the lexicon and the construction of a new paradigm (e.g., in order to correctly classify the word).


Janet's classmates and friends included Derek Gross, Graham Katz, Julie Sedivy, Beverly Spejevsky, Becca Webb, and Roberto Zamparelli in Linguistics; Peter Heeman, Marc Light, David Traum, and one of us (Massimo Poesio) among the NLP students in CS, as well as students of Dana Ballard such as Polly Pook.


Explanation is an excellent way of characterizing Alison's many professional contributions: she was always concerned with making complicated ideas understandable, both as a researcher and as a teacher.

she ran our * This is an edited version of the eulogy delivered at Alison's funeral by Greg, with contributions from her husband Richard Tobin, and her parents David and Kay Cawsey.


He also initiated a project on language resources for Asian languages which was funded by the Japanese Ministry of Education, and organized a number of workshops on the topic.

this was because she was incredibly warm and always had a charming interest in what people were up to, in research and in personal life.


My former colleagues will not tell me: Theirs is a very hush-hush operation!

we got rid of the sound room and recorded the readings on close-talking microphones.


Both these terms, now used throughout the field, were "invented" at IBM, as was the term perplexity, denoting an Information Theoretic measure of the difficulty of the recognition task.

we used the approximation But this decision did not dispose of the problem.


Learning in the presence of noise is an active research area in machine learning.

7 In fact, Aickin (1990) shows that κ is very close to this ratio when the marginal distribution over the categories is uniform, with a more substantial divergence for skewed category distributions.


The individual kernels are normalized-this plays an important role in allowing us to integrate information from heterogeneous feature spaces.

for all where φ is an (implicit) mapping from X to an (inner product) feature space F. Kernels are used inside learning algorithms such as support vector machines (SVM) or kernel perceptrons as the interface between the algorithm and the data.


This is why the numbers in Tables 5 and 6 differ from the numbers given in Belz and Reiter (2006).

to evaluate this, they ideally would like to do a study similar to Cunningham et al.


We have been involved in a number of task-based evaluations of NLG systems and components.

for Experiment 1, which focused on content-to-text, we asked three meteorologists 5 (who had not contributed to the original SUMTIME-METEO corpus) to rewrite the corpus texts for the 21 dates used in Experiment 1 (each meteorologist rewrote all 21 corpus texts), correcting and improving them as they saw fit; examples are shown in Table 1.


In summary, evaluation of NLG texts in the past has primarily been done using human subjects, either by measuring the impact of texts on task performance, or by asking subjects to rate texts.

the other method is an algorithm which is based on a spreadsheet and flowchart which one of the forecasters gave to the SUMtIME team at the beginning of the project (Reiter, Sripada, and Robertson 2003, page 499); a simplified version of this algorithm is presented in Figure 4.


A screenshot from the experiment is shown in Figure 5.

as Och points out, this is only sensible if automatic metrics are known to be very accurate predictors of text quality.


considered SUMTIME to be significantly more accurate than pCRU-random.

human-based evaluations generally need a certain number of subjects per system, so their cost goes up with the number of systems evaluated.


Wu (1997, page 158) has "been unable to find real examples" of non-binarizable cases, at least in "fixed-word-order languages that are lightly inflected, such as English and Chinese."

to see why this is, observe that each rule of the form A → B C that has complexity no greater than k can be written using a string of k e < k indices into the target nonterminal string to represent the spans' boundaries.


Figure 20 also demonstrates the re-distribution of numbers of spans after the reorganization.

the best-first analyzer takes approximately five minutes of CPU time to analyze this single rule, but processes all others in less than one second.


For any SCFG rule, if there exists a recursive partition of child nonterminals which enables tabular parsing of an input sentence w in time O(|w| k ), then there exists a recursive binary partition whose corresponding parser is also O(|w| k ).

the results on binarizability of hand-aligned data for the three language pairs are summarized in table 1.


The dashed-line stairs indicate the percentage of non-binarizable rules in our initial rule set, and the dotted line denotes that percentage among all permutations.

a parsing strategy corresponds to a recursive partitioning of the rule's children, that is, an unordered rooted tree having the child nonterminals as leaves.


However, in our situation, there is not always one single winner (two methods can perform similarly well for a given request), and there are different ways to pick winners (for example, based on F-score or precision).

in principle we could look for direct correlations between request terms and response terms.


In e-mail-thread summarization, Dalli, Xia, and Wilks (2004) applied a procedural approach where they recognized named entities and performed anaphora resolution prior to applying ranking metrics to select sentences for inclusion in a thread summary.

for example, for w = 0.5, the precision and recall values of Cluster 16 (figure 8(b)) translate to f-scores of 0.895 and 0.865 for Doc-Pred and Sent-Pred, respectively, leading to a choice of Doc-Pred.


Our experimental set-up is designed to evaluate the ability of the different responsegeneration methods to address unseen request e-mails.

this indicates that help-desk customers may have also become more tolerant of inaccurate or incomplete automatically generated replies, provided these replies are still relevant to their problem, and so long as the customers can follow up with a request for human-generated responses if necessary.


We postulate that this happens because the important terms in a request and a response are spread across Your request for a return airbill has been received and has been sent for processing.

in our case, the individual confidence (applicability) measures employed by our response-generation methods are not comparable (e.g., the retrieval score in Doc-Ret is different in nature from the prediction probability in Doc-Pred).


The first set contained responses generated by Doc-Pred and Sent-Hybrid.

even the automation of responses to the "easy" problems is a difficult task.


These chapters provide solid descriptions of applying these techniques and the performance gains that can be achieved, a useful contribution for anyone seeking to augment their existing decoder.

in "Linguistically Enriched Word-Sequence Kernels for Discriminative Language Modeling," Pierre Mahé and Nicola Cancedda demonstrate the use of string kernels for language modeling, evaluating a number of kernels including one able to integrate a range of factors (surface form, lemma, part-of-speech).


The first, titled "Enabling Technologies," focuses on research peripheral to machine translation.

i can't help but feel an opportunity was missed to lay out a clear agenda for research seeking to leverage machine learning techniques in SMT.


The bounded-memory right-corner HHMM described in this article can also be lexicalized in this way, but because head word dependencies are most straightforwardly defined in terms of top-down PCFG-like dependency structures, this lexicalization requires the introduction of additional formal machinery to transform PCFG probabilities into right-corner form (Schuler 2009).

the model described here may be promising as an engineering tool as well.


We leave it to future work to explore the interaction of our network flow method with different types of profile generation across various tasks.

for example, both Pang and Lee (2004) and Barzilay and Lapata (2005) use a minimumcut algorithm for two vastly different applications, document polarity classification and content selection, respectively.


Also as in our name disambiguation task, we additionally train and test the SVM on just the nouns in a document (rather than all words), and also on the nouns mapped to concepts (with the relevant frequencies as the feature values in both cases).

this allows us to test the ability of a distance measure to detect alternation behavior among verbs from a range of semantic classes which may differ in other respects.


For all three of our tasks, we take the simple approach of mapping each noun individually to its most specific concepts (not their hypernyms), uniformly dividing the word frequency among them.

any region of the ontology that contains some profile mass is a hierarchy rooted at some common ancestor of those profile nodes.


Other approaches, although still avoiding the difficulties of WSD, do try to capture the overall semantic "tendencies" of the set of words.

scott andMatwin (1998), McCarthy (2000), and Mohammad and Hirst (2006) take the distributional distance between concept vectors representing the texts but do not consider the ontological relations among the concepts.


In summary, our analysis in this section has shown that both distributional and ontological properties contribute to the coherence of a profile, but neither alone is indicative of the network flow performance in a particular task.

research has explored the possibility of generalizing this kind of "raw" data to a semantic profile that more appropriately reflects the coherent concepts expressed in the original set of weighted concept nodes.


It causes a complete change in how she views herself.

the current system accomplishes with some success a limited task of producing indicative summaries or short stories, but much more work is needed to create high-quality flexible summaries of literary works suitable for more than one purpose.


Such situations are referred to as accomplishments.

the results suggest that our system outperforms all baselines, including state-of-the-art summarizers.


Because the project is at the stage of an advanced feasibility study, it has not been possible to do justice to all aspects of the problem.

this suggests that resolving pronominal anaphoric expressions is crucial to summarizing short stories.


All subjective questions are multiple-choice questions.

the comparison is not wholly fair, but-in the absence of systems similar to ours-it was the only possibility to compare our summarizer with the state-of-the-art in the community and to verify whether genre-specific methods are useful in summarizing fiction.


The 25 unique beginners have been shaded, and the two graphics show how the hierarchy evolved between the two WordNet versions used in this study.

the last row gives the total score of the best systems on the three tasks.


SemCor is the largest publicly available corpus of sense-tagged text, and has only about a quarter million sense-tagged words.

this is partly because of the lack of sufficient data.


But the results do indicate that a better set of semantic classes may lead to significantly better WSD accuracy.

our problem becomes finding a solution to the linear equation w = WS × s. Using the Moore-Penrose pseudoinverse, WS + , we find a solution s = WS + × w. This solution minimizes the distance |WS × s − w|.


A candidate set for architourist, a blend of architecture and tourist, is given in Table 1.

the percentages reported in this row thus serve as an upper bound on the task for each data set.


(Gries [2006] reports a similar finding.)

because our various features have values falling on differing ranges, we first normalize the feature values by subtracting the mean of that feature within that candidate set and dividing by the corresponding standard deviation.


He argues for a neo-Davidsonian knowledge representation following Schank (1980), Schubert (1976), and others, and at the same time he sets his approach apart from the knowledge representation proposals one can find in Semantic Web representation languages such as RDF and OWL.

chapter 5 provides the author's conclusions, suggesting technological and theoretical enhancement to the current version of NKRL.


some of these discussions would have benefited from a more in-depth treatment.


Again, however, their approach does not make use of the theme/rheme distinction, and does not attempt to predict pitch accent type or edge tones; in addition, they report frequent errors on auxiliaries and negatives (e.g., no), which we have found to be important for highlighting trade-offs prosodically.

in brief, the idea behind the strategy is to select the top-ranked flight, along with any other highly ranked flights that offer a compelling trade-off-that is, a better value (for the user) on one of its attributes.


Finally, we may observe that although the APML voice had fewer boundary corrections than did the ALL voice, the difference did not reach significance, suggesting that there is room for improvement in how boundaries are handled in the APML voice.

note also that the n-gram models had a L+H* accent on good, in contrast to the target tune's H*, but both choices were considered acceptable.


This must have been due to the different natures of the training and test data.

obtain a text x from S. If SS is empty, the text x is put into SS and another text x is obtained from S. The texts of SS are already sorted and are indexed by i = 1 .


Readability in this article signifies the latter, for both a mother tongue and a second language.

our method shows that even with rough two-level training data, high correlation is achievable.


Most of the features that we implemented for answer re-ranking are based on word overlap between part of the question and part of the answer.

the potential importance of sentence structure for improved ranking can be exemplified by the following two questions from our set.


The first reason is that the improvement on MT accuracy we achieved by tree re-structuring indicates that the benefit we obtained from structure reuse triumphs the problem of tree over-generalization.

we ask what kinds of tree structures, tree labels, and word alignments are best suited for improving end-to-end translation accuracy.


All in all, this is a very interesting book, both clear and complete, and written by a researcher who has been an important contributor to the field.

the last chapter of the book, Chapter 7, entitled Language Models for Latent topic Analysis, presents two widely used topic models, namely PLSA (Probabilistic Latent Semantic Analysis) and LDA (Latent Dirichlet Allocation), and their application to information retrieval.


This chapter is slightly more technical than the previous ones.

the author reviews document-specific smoothing methods, based on document clustering and document expansion, the use of n-gram models and Markov random fields, as well as the full Bayesian query likelihood and the translation models.


In addition, we use language not only as a vehicle for factual information exchange; speakers may realise all sorts of other intentions with their words: They may want to convince others to do or buy something, they may want to induce a particular emotion in the addressee, and so forth.

in their model of interpersonal language (the Linguistic Categorization Model), Semin and Fiedler (1988) make a distinction between different kinds of verbs and their position on the concrete-abstract dimension.


Another problem is that it is hard to find an evaluation metric for word alignment that correlates well with translation quality because the relationship between alignment and translation is still not quite clear.

cherry and Lin (2006) show that introducing soft syntactic constraints through discriminative training can improve alignment quality.


besides the obvious caveat discussed previously regarding distributional similarity, we find that the other most important factor affecting the performance of these methods is the choice of distributional ingredients-that is, the features used to formulate the distribution of the extracted units.


In recent years, a number of such fora have been made available to the automatic paraphrasing community (Inui and Hermjakob 2003;Tanaka et al.

this must be an important consideration when building the proposed entailment engine.


Mathematically, Quirk, Brockett, and Dolan's approach to sentential paraphrase generation may be expressed in terms of the typical channel model equation for statistical machine translation: The equation denotes the search for the optimal paraphrase E p for a given sentence E. We may use Bayes' Theorem to rewrite this as: where P(E p ) is an n-gram language model providing a probabilistic estimate of the fluency of a hypothesis E p and P(E|E p ) is the translation model, or more appropriately for paraphrasing, the replacement model, providing a probabilistic estimate of what is essentially the semantic adequacy of the hypothesis paraphrase.

in addition, keyword-based heuristics need to be employed to prevent inaccurate merging of constituent nodes due to, say, alternations of active and passive voices among the The merging algorithm.


In fact, it may even lead to research being duplicated across communities.

other examples of possible correspondences could include the same stem expressed as different parts-of-speech (such as significance, significantly ) or two non-synonymous verbs (such as this is also, this also marks ).


The phrase extraction heuristic used to compute N P and N M from the respective alignments is the same as that employed by Bannard and Callison-Burch (2005) and illustrated in Figure 8.

instead of fully lexicalized phrasal correspondences as in (Bannard and Callison-Burch 2005), the fundamental units of translation (and paraphrasing) are hierarchical phrase pairs.


(2005) attempt to deal with this problem by using a fuzzy algorithm to cluster speakers; this assigns each speaker a distribution over conversations rather than making a hard assignment.

a straightforward reading of the transcript, however, might interpret it as a response to Gale's statement immediately preceding.


We describe the classifier's performance in terms of raw accuracy (correct decisions/total), precision and recall of the same class, and F-score, the harmonic mean of precision and recall.

name mention features are critical for our model.


Lexical cohesion refers to the degree of semantic relatedness observed among lexical items in a document.

in this article we are interested in creating a compression model that is appropriate for both documents and sentences.


Their corpus consisted of four passages randomly selected from a set of sample Graduate Management Aptitude Test (GMAT) reading comprehension tests.

we thus score chains according to the number of sentences their terms occur in.


Such a condition is easily captured with the following ILP constraint: As an example, consider the first discourse in Figure 2.

accompanying each text were eight multiple-choice questions, each containing five possible answers.


The majority of sentence compression approaches only look at sentences in isolation without taking into account any discourse information.

table 1 shows the compression rates (CompR) for the three systems and evaluates the quality of their output using grammatical relations F1.


The second document is about Mrs Allan and contains many references to her.

they extend Knight and Marcu's (2002) noisy-channel model so that it can be applied to entire documents.


Each word is assigned a context weight based on the number of links to the local context and the importance of each relation type.

due to the high level of polysemy inherent in WordNet, algorithms developed for computing lexical chains must adopt some strategy for disambiguating word senses.


Finally, if the current item is a maximal chain c = n 1 , .

each fragment that is excised from some source tree must obligatorily be adjoined back into that tree, at the point from which it was removed.


The factorization algorithm can then be applied to the new grammar.

we can conclude that the increase in complexity stems from the multi-component nature of the formalism rather than from the power added by an unconstrained adjunction operation.


We now analyze the running time of the factorization function in Figure 18.

because each variable occurs at most four times in the formula, the total number of links is bounded by pn 12 .


This can be accomplished by performing a first-pass grammar transformation in which a copy of each elementary tree set is added to the grammar for each distinct, maximal, nonconflicting set of links appearing in the tree set.

in spite of the provable difficulty of the recognition problem, we offer several algorithms that can substantially improve processing efficiency.


2005;Ganchev, Gillenwater, and Taskar 2009); discovery of paraphrases (Bannard and Callison-Burch 2005); and joint unsupervised POS and parser induction across languages (Snyder and Barzilay 2008).

using this decoding we include an alignment link i − j if the posterior probability that word i aligns to word j is above some threshold.


To see the effect of this constraint, consider the following example with a source sentence s 1 s 2 and a shallow-1 grammar defined by these four rules: There are two derivations R 1 R 2 and R 1 R 3 R 4 which yield identical translations.

the hypotheses selected by LMBR are in almost all cases exactly the same regardless of the choice of semiring.


Because syntactic constituents are often moved together across languages during translation (Fox 2002), we particularly study how linguistic knowledge affects syntactic constituent movement.

when we combine them at the model level, we achieve an absolute improvement of 0.83 and 1.13 BLEU points over BwR and LAR, respectively, which are both statistically significant (p < 0.01).


home in the fifth example directly hit the title of relevant Web pages, while the SMTbased expansion terms do not improve retrieval results.

our results show that the combination of translation and language model of a state-of-the-art SMT model produces high-quality rewrites and outperforms the expansion model of Cui et al.


The translation model proposes expansion candidates, and the query language model performs a selection in the context of the surrounding query terms.

in this article, instead of deploying NLP tools or ontologies, we apply NLP ideas to iR problems.


What we see for each corpus is that the conditional entropy grows over these successive subsets until it approaches the conditional entropy of the corpus as a whole.

a symbol system that was completely rigid-had an entropy of 0-would convey no information whatsoever.


But again, we focus here on the issues that relate to computational linguistics.

c r ≥ 4.89 and U r ≥ 1.37, yielding classification of the system as a logographic writing system.


The acoustic scores of the two hypotheses were virtually identical, and the correct choice happened to come second.

i had to take this list and expand it with additional categories for ambiguous words and add in syntactic and semantic features and semantic interpretation rules for the words that needed them.


This suggests that as N approaches infinity, almost all the within-dictionary words would have appeared more than once, and the vocabulary growth would mainly be the growth of hapaxes formed by random alphanumeric strings, and the HVR curve would gradually approach its horizontal asymptote 1.

a computer simulation shows that as the text size continues to increase, the hapax/vocabulary ratio would approach 1.


In Example (6a), police officers could be patrol police officers, and investigators could be university researchers.

in Example (6a), police officers could be patrol police officers, and investigators could be university researchers.


For the sake of convenience, we use the same names for category operations and dependency structure operations.

a difference is that the dependency LM is used bottom-up in our MT system, whereas the structured LM is used left-to-right in aSR.


Condition 3 is met according to Equation (4) and condition 3 of the fixed structure.

a similar strategy was employed by DeNeefe and Knight (2009).


The rows and columns of the three matrices resulting from n-mode matricization of a third-order tensor are vectors in spaces whose dimensions are the corresponding column and row elements.

most problems encountered by DSms in tackling this challenge are specific instances of more general issues concerning the possibility of representing symbolic operations with distributed, vector-based data structures (markman 1999).


For all our models, the label sets W 1 = W 2 contain 30,693 lemmas (20,410 nouns, 5,026 verbs, and 5,257 adjectives).

we use the memory-efficient MET(1) algorithm of Kolda and Sun (2008) as implemented in the Matlab Tensor Toolbox.


model, although highly accurate in its predictions, has much lower coverage and needs a semantically annotated corpus as a basis.

the SYN PRIMARY setting has predicates that are verb lemmas, such as cause, and argument positions that are grammatical functions (subj).


These exercises are reasonably chosen and solutions are technically well prepared and exhaustively explained with an admirable degree of clarity.

increasing the level of abstraction at the systems level, the author then advances to comparing GATE and UiMA, two alternative architectures for text analytics.


2007) is also mature enough to be taught and reconstructed in didactic code in an effective manner.

the next two chapters (8 and 9) cover syntax and parsing.


Given a headword and a context window, a statistical approach identifies the word combinations from the context inclusive of the head word with significant lexical statistics as the collocations; a syntactic approach resorts to syntactic knowledge acquired from a parser to refine the candidates, assuming that collocation words are syntactically dependent; a semantic approach exploits semantic relations such as synonyms obtained from WordNet to overcome the inaccuracies caused by insufficient statistics; a categorical approach adopts a set of carefully tailored rules to identify collocations of different types, which may potentially achieve better results, but the method turns out to be less generalizable.

the general statistical methods based on co-occurrence statistics are depicted, followed by the techniques for identifying specific types of proper nouns based on their particular formation patterns.


Although the preface says that the book is aimed at both engineering and humanities students, I suspect that readers without any mathematical background would have difficulty with some of the more formal sections.

this is followed by a discussion of concepts from HCI-style usability evaluations and how they can be applied to spoken dialogue systems, and then a section dealing with semiautomatic evaluation and the role of standardization.


Chapter 4 contains case studies illustrating a range of dialogue control strategies and models.

the necessary background knowledge is not great, and in general the book is clearly written and understandable; it is suitable as both an introduction to this research area and a survey of current stateof-the-art techniques.


The success of this approach is best exemplified by his machine-aided discovery of a possible genetic relationship which would otherwise have eluded human discovery.

the low-level machine-discovered generalizations can then be used as the basis for more interesting manually created generalizations.


The sorted values of P* (in gray) are seen to be approximately uniformly distributed as expected, whereas the values of P are not.

this constraint further constrains the vocabulary by removing a multitude of names and replacing them with a much smaller and less diverse set.


This provides a basis for investigating whether a script set is unlikely to be an example of a random system drawn from a non-equiprobable distribution of unigram occurrence.

whereas there is unigram-to-unigram dependence in a language, there is no such dependency in a random system.


For this set of much more relevant nonlinguistic systems, Vidale demonstrates that the average number of different signs is only about 44, a far cry from the 400 or so signs in the Indus script.

sproat, Liberman, and shalizi (in a blog by Liberman [2009]) and sproat at EMNLP'09 undertake the exercise of knocking down the straw man ("similarity in conditional entropy by itself implies language") and present artificial counterexamples (e.g., having Zipfian distribution) with conditional independence for bigrams (sproat 2010).


Several participating systems produced update summaries indistinguishable from humangenerated summaries when measured using ROUGE.

tAC 2008 also used a manual overall responsiveness score.


In the 10p case, the new opinion words extracted by our approach could cover almost 75% of the whole opinion set whereas the corresponding seed words only cover 8% of the opinion words in the data (see the Init line).

in the dependency grammar, a syntactic relation between two words A and B can be described as A (or B) depends on B (or A).


Besides, the statistical estimation can be unreliable if the corpus is small, which is a common problem for statistical approaches.

they did not tackle the prediction of sentiment polarities of the found subjective adjectives.


(Feature Introduction) there is a type Intro(F) ∈ TYPE such that:  A type signature is a structure TYPE, , FEAT, Approp , where TYPE, is a type hierarchy, FEAT is a finite set of features, FEAT and TYPE are disjoint, and Approp is an appropriateness specification.

in S 4 , {q 2 , q 3 } ≈ {q 4 } and {q 5 } ≈ {q 6 }, and after coalescing these two pairs, the result is S 5 which is non-redundant.


This casting is not part of the core of a signature, but rather a device that enables advanced module communication.

again, note that type constraints are not addressed in this work.


Define NodeCoalesce (S, q, q )  The input to the name resolution algorithm is a signature module and its output is a signature module whose typing function, T, is total.

this desideratum is not absolute-it is restricted to cases where the combination formulates a simple union of data.


We recruited six pairs of players, who each received US $10 for completing the data collection.

we measure pitch, energy (local root mean squared measurement), and duration of each case of do you have.


In Section 4.2, we find that players tend to interrupt more often at the end of a card or a poker game when they are given more time.

it tells us how much the odds of a task interruption decrease when the classifier predicts negative (non-interruption).


A disadvantage of character-based models is the use of limited contextual information.

when it is applied to English POS-tagging using feature templates from Collins (2002), it gave similar accuracy to the Table 25 Comparison of dependency accuracies between phrase-structure parsing and dependency parsing using CTB5 data.


For English constituent parsing using the Penn Treebank, the best performing transition-based parser lags behind the current state-of-the-art (Sagae and Lavie 2005).

this decoding process can be expressed as an instance of the generic algorithm in Figure 1.


The corpus was created in the larger context of the TALK project 1 and is also known as the SAMMIE corpus (Kruijff-Korbayová et al.

when examining the distributions of user replies per system turn for the bigram model, we can see that 25% of the state-action pairs have zero frequencies.


The database used in the WOZ experiments contained over a million songs and is searched with partial string match.

the use of WOZ data allows us to develop optimal strategies for domains where no working prototype is available.


particular, we extract a supervised policy, reflecting human (wizard) performance on this task (see Section 3.3).

user simulations used for testing should be more accurate with respect to the data in order to test under realistic conditions (e.g., Möller et al.


attempt to capture likely properties about adjacent arcs in the tree via their lexical and part-of-speech information.

dependency to allow the guided model to learn weights relative to different surface syntactic environments.


Perhaps the most interesting reason to study the canonical graph-based and transition-based models is that even though they appear to be quite different theoretically (see Section 2), recent empirical studies show that both obtain similar parsing accuracies on a variety of languages.

the dependency graph given at termination is guaranteed to be acyclic and projective and to satisfy dependency graph conditions 1-3, which means that it can always be turned into a well-formed dependency graph by adding arcs (0, i, l r ) for every node i = 0 that is a root in the output graph (where l r is a special label for root modifiers).


For the guided graph-based model, which we call MST Malt , this feature representation is modified to include an additional argument G Malt x , which is the dependency graph predicted by MaltParser on the input sentence x.

these features provide the lexical and part-of-speech context for determining whether a given arc label is suitable for a given head and modifier.


This is expected, as the inference algorithm and feature representation should not prefer one type of arc over another.

the new feature representation will map an arc and the entire predicted MaltParser graph to a high dimensional feature representation, f(i, j, l, G Malt x ) ∈ R k+m .


Tok = number of tokens (×1000); Sen = number of sentences (×1000); T/S = tokens per sentence (mean); Lem = lemmatization present; CPoS = number of coarse-grained part-of-speech tags; PoS = number of (fine-grained) part-of-speech tags; MSF = number of morphosyntactic features (split into atoms); Dep = number of dependency types; NPT = proportion of non-projective dependencies/tokens (%); NPS = proportion of non-projective dependency graphs/sentences (%).

we can expect error propagation from shorter to longer overlapping arcs and from preceding to succeeding arcs.


What we have shown here is that, on a number of data sets, our algorithmic expectations about two widely used dependency parsing paradigms are confirmed.

the precision curve for MSTParser is much flatter than MaltParser, which sees a drop of up to 40% in the extreme.


These methods directly minimize the parsing complexity of the resulting deduction system.

each edge in G comes from a clique for some vertex v in G , so the edge has both its endpoints in any node of T corresponding to a node of T that contained v. T is a valid tree decomposition of G .


Given friction between the moving parts, the balance will have limited sensitivity, so that for some y that is fractionally heavier than x, the difference in weight will fail to register, and similarly for a z that is fractionally heavier than y.

even if you are well-versed in the literature on vagueness, you are likely to learn something new.


Chapter 2 contains an interesting excursus on how apparently clearcut notions such as 'species' become more indeterminate on closer inspection, and Chapter 3 points out that attempts to turn vague terms such as obesity, poverty, and intelligence into precisely defined notions suitable for scientific investigation are subject to arbitrariness and residual vagueness.

if the light is bright at step n, then tolerance dictates that it will also be bright at step n + 1.


For the more inflected Slavic and Romance languages considered in this book, the tag sets include as many as 4,000 tags, each marking a full suite of morphosyntactic features, such as tense, case, or number.

at the culmination of their book, in Chapter 7, Feldman and Hana report a 6% absolute improvement, 79.7% vs. 73.5% labeling accuracy, when using a Czech morphological analyzer projected to Russian as opposed to training a statistical analyzer directly on a small sample (1,758 words) of hand-annotated Russian.


Chapter 2 then presents writing as the first case of language technology, contrasting writing with other symbolic systems.

indeed, for those sets of symbols for which we have limited data and no linking to other texts, we may never know whether they are even writing systems.


Recall also that the individual word ratings provided by a single researcher were reviewed by a larger committee, mitigating some of the subjectivity involved.

additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.


As with the adjectives, there are more negative nouns and verbs than positive ones.

although such classifiers perform very well in the domain that they are trained on, their performance drops precipitously (almost to chance) when the same classifier is used in a different domain (Aue and Gamon [2005]; see also the discussion about domain specificity in Pang and Lee [2008, section 4.4]).


Soricut and Brill (2006) develop a statistical model by extracting (in an unsupervised manner) QA pairs from one million FAQs obtained from the Web.

their features, however, are based on counting matches or events defined heuristically.


In order to motivate research on stability in AGC, 2 we analyzed a large, publicly available newspaper corpus and found that (1) genres and topics do correlate substantially and (2) these correlations vary substantially over time.

we considered  Karlgren and Cutting 1994).


First, an obvious class of competitors is textbooks such as Jurafsky and Martin (2008) and, to a lesser extent, Manning and Schütze (1999) (which covers only statistical techniques), at least one of which (and probably both) are already on the professional reader's bookshelf.

this reviewer only encountered minor typos such as "Finite-sate" (p. 57).


Both Sections 2 and 3 include example sentences from English and analyses for those sentences that give the reader an insight into how typelogical grammar is applied to linguistic data.

this section, like the others in Part II, is short and has room only for the definitions and some linguistic examples.


That being said, this book contains a good introduction to type-logical grammar and its first part would make a good textbook in an advanced course on the theory of type-logical grammar.

section 4, titled "Processing," introduces proof nets for Lambek categorial grammar as a more natural representation of typelogical syntax and semantics.


This part is likely to be of relevance to linguists who are interested in the descriptive capabilities of type-logical grammar, but little attention is paid to computational aspects in this part.

the arguments made often have little relevance to mainstream computational linguistics.


CLIR approaches are in general presented together with their statistical models, whose understanding does not require more than elementary calculus and probability theory.

as specific books on CLIR have been limited so far to edited collections of scientific papers (Grefenstette 1998), it was definitely time for the first monograph on the topic.


Recently, other variations on the theme have been proposed that address non-textual documents, such as image, music, and speech retrieval.

the book does not present algorithms or data structures to implement the models, so it might not be a sufficient resource to build an effective CLIR system.


Taking those two data points, 7 we are able to hypothesize that the real number of Turkers is between 15,059 and 42,912.

at least one university legal department was sufficiently concerned that Turkers working for several months would claim employee status and demand health and other benefits that they refused to allow grant funds to be expended on MTurk (personal communication, E. Hovy).


as in any scientific endeavor involving humans, there is an unspoken ethical dimension involved in resource construction and system evaluation, and this is especially true of MTurk.


Several functions are provided to balance words of different lengths.

a character sequence can be divided into two adjacent subsequences.


In other words, if X is selected as a word, its proper subsequences will not continue to be regarded as potential words.

for example, consider the character sequence X.


The maximum order of n-grams in the test of TONGO was six.

the selected word candidates move from the candidate lexicon to the core lexicon, which is termed forward selection.


For example, there are two character sequences: ABC and BBC.

the records become 1, and BBC(1) after Adjustment.


The approach consists of multiple order models and an escape strategy that is used to transition from the higher order model to the lower one.

the F-measure was 0.57 without a given length (VE2).


According to the simple regression analysis, the proper exponents can be approximately predicted.

the algorithm takes polynomial time over the length of the character sequence being processed.


This method provides a principled way to produce utterances matching the linguistic style of a specific corpus (e.g., of an individual author) without any overgeneration phase.

pERSONAGE's content pool is based on a database of restaurants in New York City, with associated scalar values representing evaluative ratings for six attributes: food quality, service, cuisine, location, price, and atmosphere.


Results for overall user satisfaction were consistent with the results from the reading experiment: Participants chose the UMSR system more often than the SR system when asked which system they would recommend to a friend.

user: Ok, can you repeat the options for business class flights that arrive around 4:00 pm?


The resulting option tree determines how different options relate to one another, and which ones are most attractive for the user.

for the experiments reported here, templates were used instead of the full generation pipeline, for reasons of robustness.


For clustering, they added an "associative mode" in which association rules among attributes are automatically determined and the summary statements produced describe the associations.

they evaluated only the first system turn in the dialog for the different conditions presented.


Requirements appear in α 3 and α 1 β 1 in increasing order of link distance.

following Sikkel (1997), items are sets of syntactic structures and tuples are a shorthand notation for such sets, as seen in the previous example.


Let T be a dependency tree headed at node h, and properly formed for MG k .

in any of these treebanks, the MG k parser can parse every sentence with gap degree at most k in time O(n 3k+4 ).


A node labelled i is a descendant of j in B if and only if i → j in T. The projection of a node in a binarization is the set of reflexive-transitive children of that node.

a postprocessing mechanism is needed to determine which of the generated structures are, in fact, valid trees.


For all the data sets described in Sections 5.1 and 5.2, each Chinese review was translated into an English review, and each English review was translated into a Chinese review.

we will show in the experiments that the English classifier and the Chinese classifier disagree on many unlabeled examples, which can also guarantee the success of the co-training approach.


As can be seen in Tables 1 through 8, no matter which machine translation service is used, the proposed co-training approach (CoTrain) outperforms all baseline methods on the overall accuracy metric and most other metrics on the two test sets.

(8) Add the reviews in E en E cn with the corresponding labels to L. From the point of view of the algorithm, the unlabeled reviews are added to the model during the bootstrapping phase, and only for these reviews are the labels obtained using an average of the normalized prediction values determined by the component classifiers.


This book also has five appendixes that are of great interest to readers.

it highlights necessary issues to be aware of in NLP research and application building.


In general the structure and content of this book is appropriate for researchers from neighboring fields (including NLP) who want to get a quick taste of what computational modeling of human language acquisition is all about.

a short introductory text on computational modeling of human language acquisition seems particularly apt at this time.


The authors first give convincing facts about the importance and availability of large data corpora.

advantages and disadvantages of each design pattern are explained at length.


These bits have to "summarize" that piece of the image.

we base our discussion on the work of Goldwater and Griffiths 2007, as it dramatically shows the need for some informative prior.


In addition, the correlations for error distributions within a translation output (as in Section 4.2) are presented as well in the rightmost column, and it can be seen that, as in the previous experiments, they are high for each of the translation outputs.

differences of word order in the hypothesis with respect to the reference are taken into account only by WER and not by PER.


Then, the total hypothesis length of the document is N hyp = k N hyp k and the total reference length is is defined as the length of the reference sentence with the lowest sentence-level error rate as shown to be optimal with respect to the correlation with the human evaluation score's adequacy and fluency (Leusch et al.

the noun reordering error Bush is not resolved by the POS-based reorderings of verbs, whereas it is at the correct position in the hierarchical system output.


By using a distance-based subgrouping method the Levenshtein is susceptible to exactly the same problems that classical lexicostatistics faced (Swadesh 1952).

the correct classification of triplets is the simplest possible subgrouping task, with only four possible subgroupings: language A is more similar to language B than C (A,B|C), A is closer to C (A,C|B), B is closer to C (B,C|A), and no language is closer to the other (A,B,C).


Language of the material was the only characteristic of the material comparable across studies and domains.

this result is thus most likely a chance effect due to the specifics of the data set, and therefore hard to interpret in a meaningful way.


The traditional method has long been criticized for being "unsystematic, haphazard, and impos [ing] an impossible information-processing burden on the reviewer" (Hunter and Schmidt 1990, p. 488).

our recommendations thus apply only directly if authors use percentages to measure coder agreement.


A general recommendation would be to adjust the minimum level of agreement depending on the annotation purpose-as is already the practice in the computational linguistics community as indicated by our data.

inter-annotator agreement decreased with a rising number of categories in the coding scheme.


From this graph, we can see that our guidelines are indeed effective in improving the inter-annotator agreement.

our annotation interface handles events of this type by allowing the annotators to specify which interpretation they are giving.


both single (i.e., destroyed one missile) and aggregate (i.e., destroyed all missiles) events happened.

they were not to use knowledge of the future when annotating a historical article.


Performance achieved with the Bikel (2004) parser and relabeled brackets.

not inserting it, however, makes the annotator's task simpler.


During the annotation process, we encountered a number of NPs that were difficult to bracket.

give some examples that were more difficult for our annotators: (NP week-end sales license) (NP furniture sales license) this difficulty in large part comes from the lack of context that we are given.


In most cases, the head is either the right-most noun, or inside the right-most NML node.

even after this process, there were still 48 word sequences by type (201 by token) that were inconsistent.


Use surname only, remove all pre-and post-modifiers.

when we modified condition (1) of our decision rule (Algorithm 2) to obtain Algorithm 3, the accuracy increased to 78%.


We then validate the performance of the predictor on an independent data set using human judgments as gold standard, achieving high accuracy.

jordan and Walker (2005) show that, in task-oriented dialogs, over-specified references (including more attributes than needed to uniquely identify the intended referent) are more likely for certain dialog states and communicative goals.


By incorporating such an interpolation mechanism, we anticipate an amelioration in performance further supporting the use of half-context in language models.

but that is not possible in a whole-context model.


exemplar-theoretic Brown model P (Half) exemplar-theoretic Half-Context model (Equation 1) P (Whole) whole-context analogue of Equation 1 P (KN-Brown) interpolation of P (KN) with P (ET-Brown) P (KN-Half) interpolation of P (KN) with P (Half) P (KN-Whole) interpolation of P (KN) with P (Whole) 1,024 classes (the same number of classes as the combined left and right context clusters in the 2 × 512 HC model) were derived from the training data.

as we will see subsequently the test is sufficiently sensitive for the key results of the article.


This line of research is further pursued by Litman (1994Litman ( , 1996, who incorporates machine learning techniques to derive classification models automatically.

a high positive value of pitch slope corresponds to a rising intonation; a value close to zero, to a flat intonation; a high negative value, to a falling intonation.


In the second part of the Cards game, each player saw a board of 12 cards on the screen ( Figure A1b), all initially face down.

tables 4 through 8 summarize the full feature set.


Then L is a probability distribution over A * , because L is positive and L 1 = 1.

current techniques such as latent semantic analysis work well at the word level, but we cannot extend them much beyond this, to the phrase or sentence level, without quickly encountering the data-sparseness problem: There are not enough occurrences of strings of words to determine what their vectors should be merely by looking in corpora.


In R n , given a specific basis, we can write two vectors u and v as sequences of numbers: .

in this example, we have ξ(cat) = (0, 2, 3) and ξ(big) = (1, 3, 0).


One important type of entailment rule specifies entailment between propositional templates, that is, propositions where the arguments are possibly replaced by variables.

for the negative features it seems that the precision of VerbOcean features is very high (though they are sparse), and the precision of WordNet antonyms and co-hyponyms is lower.


Nevertheless, using a graph for global learning of all entailment relations within a set of predicates, rather then between pairs of predicates, has attracted little attention.

in this section we define a structure termed the entailment graph that describes the entailment relations between propositional templates (Section 3.1), and a specific type of entailment graph, termed the focused entailment graph, that concentrates on entailment relations that are relevant for some pre-defined target concept (Section 3.2).